A Hybrid Residual Network and Long Short-Term Memory Method for Peptic Ulcer Bleeding Mortality Prediction

Qingxing Tan, Andy Jinhua Ma, Huiqi Deng, Vincent Wai Sun Wong, Yee Kit Tse, Terry Cheuk Fung Yip, Grace Lai Hung Wong, Jessica Yuet Ling Ching, Francis Ka Leung Chan, Pong Chi Yuen

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

13 Citations (Scopus)


The prediction of patient mortality, which can detect high-risk patients, is a significant yet challenging problem in medical informatics. Thanks to the wide adoption of electronic health records (EHRs), many data-driven methods have been proposed to forecast mortality. However, most existing methods do not consider correlations between static and dynamic data, which contain significant information about mutual influences between these data. In this paper, we utilize a deep Residual Network (ResNet) consisting of many convolution units, which can jointly analyze different variables, to capture correlation information in and between static and dynamic variables. Furthermore, the Long Short-Term Memory (LSTM) method is used to extract temporal dependencies information from dynamic data. Finally, a deep fusion method is used to integrate these different types of information to improve mortality prediction. Experiment results on Peptic Ulcer Bleeding (PUB) mortality prediction show that the proposed method outperforms existing methods and achieves an AUC (area under the receiver operating characteristic curve) score of 0.9353.

Original languageEnglish
Title of host publication2018 AMIA Annual Symposium Proceedings
PublisherAmerican Medical Informatics Association
Number of pages10
Publication statusPublished - Nov 2018

Publication series

NameAMIA Annual Symposium proceedings
PublisherAmerican Medical Informatics Association
ISSN (Print)1559-4076
NameAMIA Proceedings

Scopus Subject Areas

  • Medicine(all)


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